LLaMA examines efficient foundation models

A family of language models emphasized training efficiency and publicly available training data.

Hugo Touvron and colleagues

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The contribution

The original LLaMA paper introduced models spanning 7 to 65 billion parameters and examined how training choices affected performance. It showed that smaller models trained extensively could be competitive on the reported benchmarks.

What this does not establish

The original release had restricted research access. The word “open” in the title does not establish an unrestricted open-source license or full reproducibility.

Why this date?

The first arXiv submission was 27 February 2023.

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The original work

LLaMA: Open and Efficient Foundation Language Models

Authors: Hugo Touvron and colleagues
Publication: arXiv:2302.13971
Source type: Preprint

Paper and version history

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Cited By

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GPT-4 Technical Report (2023)
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Mistral 7B (2023)
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024)
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Citations

Works in the Atlas cited by this entry.

Computing Machinery and Intelligence (1950)
Listed in the source bibliography. Check the reference list.
Finding Structure in Time (1988)
Cites the later 1990 journal version. Check the reference list.
Long Short-Term Memory (1997)
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Attention Is All You Need (2017)
Listed in the source bibliography. Check the reference list.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
Listed in the source bibliography. Check the reference list.
Language Models are Unsupervised Multitask Learners (2019)
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Language Models are Few-Shot Learners (2020)
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Scaling Laws for Neural Language Models (2020)
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Training language models to follow instructions with human feedback (2022)
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